Biomakers useful in liver fibrosis diagnosis

ABSTRACT

Identification of urokinase-type plasminogen, matrix metalloproteinase 9, and β-2-microglobulin as novel biomarkers associated with liver fibrosis and uses thereof in diagnosing liver fibrosis.

RELATED APPLICATIONS

This application claims priority to U.S. patent application Ser. No.12/628,758, filed on Dec. 1, 2009, which claims priority to U.S.Provisional Application No. 61/119,077, filed on Dec. 2, 2008. Thecontents of the prior applications are hereby incorporated by referencein their entirety.

BACKGROUND OF THE INVENTION

Liver fibrosis involves excessive accumulation of extracellular matrixproteins (e.g., collagen) on liver cells, resulting in scar tissues. Itoccurs in most chronic liver diseases, such as metabolic liver diseasesand those associated with hepatitis B or C infection and alcoholconsumption. Advanced liver fibrosis leads to cirrhosis, liver cancer,liver failure, and portal hypertension.

Currently, liver biopsy is an optimal approach for detecting liverfibrosis and determining its severity. However, liver biopsy, aninvasive procedure, is not an ideal diagnostic approach.

Non-invasive serology assays, based on fibrosis-associated serumbiomarkers, have been developed for diagnosing liver fibrosis. Theaccuracy and sensitivity of such assays rely heavily on the biomarkersused. Thus, it is of great importance to identify reliable biomarkersthat differentiate fibrosis patients from non-fibrosis humans with highsensitivity.

SUMMARY OF THE INVENTION

The present invention is based on an unexpected identification of threenew serum biomarkers, i.e., urokinase-type plasminogen activator (uPA),matrix metalloproteinase 9 (MMP9; GenBank Accession Number), andbeta-2-microglobulin (β-2MG), for diagnosing liver fibrosis.

Accordingly, one aspect of this invention features a method fordiagnosing liver fibrosis based on the expression level of one or moreof the three biomarkers listed above, and optionally in combination withone or more additional serum biomarkers, such as glutamic oxaloacetictransaminase (GOT), glutamic pyruvic transaminase (GPT), andalpha-fetoprotein (AFP).

The just-described diagnostic method includes at least four steps: (i)obtaining a blood sample from a human subject suspected of having liverfibrosis (e.g., a hepatitis B or C virus carrier, or a patient sufferingfrom an alcohol-related liver disease or a metabolic liver disease),(ii) detecting in the blood sample the expression level of one or moreof the biomarkers listed above, (iii) calculating a disease score basedon the expression level, (iv) determining whether the subject hasfibrosis based on the disease score. Optionally, the diagnostic methodincludes, after step (iv), additional step (v): assessing the subject'sfibrosis stage based on the disease score as compared to pre-determinedcutoff values indicating different fibrosis stage. The term “diagnosing”used herein refers to determining presence/absence of liver fibrosis ina subject (e.g., a human) or assessing the disease stage in a subject. Ablood sample can be any sample obtained from blood, such as a serumsample or a plasma sample.

The disease score can be determined by subjecting the biomarkerexpression level to discriminant function analysis, ridge regressionanalysis, or logistic regression analysis.

Another aspect of this invention features a diagnostic kit containing atleast two antibodies (e.g., whole immunoglobulin molecules), one beingspecific to uPA, MMP9, or β-2MG and the other being specific to uPA,MMP9, β-2MG, GOT, GPT, or AFP. These two antibodies have differentantigen-specificities. Preferably, the kit consists essentially of theantibodies mentioned above, i.e., containing only antibodies specific toantigens to be detected (e.g., fibrosis-associated biomarkers) fordiagnosing liver fibrosis. In one example, the kit contains an anti-uPAantibody, an anti-MMP9 antibody, and an anti-β-2MG antibody.

Also within the scope of this invention is use of any of antibodiesmentioned above in diagnosing liver fibrosis or in manufacturing a liverfibrosis diagnostic kit.

The details of one or more embodiments of the invention are set forth inthe description below. Other features or advantages of the presentinvention will be apparent from the following detailed description ofseveral embodiments, and also from the appended claims.

DETAILED DESCRIPTION OF THE INVENTION

In one aspect, the present invention relates to a method for diagnosingliver fibrosis based on a subject's blood level of biomarkers uPA, MMP9,β-2MG, a combination thereof, or a combination of (a) one or more ofuPA, MMP9 and β-2MG, and (b) one or more additional fibrosis biomarkers(e.g., GOT, GPT, and AFP). This method can be applied to a human patientin need to determine presence/absence of fibrosis in that patient or hisor her fibrosis stage. The human patient can be a carrier of HBV or HCV,or a patient suffering from an alcohol-related disease (e.g., fattyliver and alcoholic hepatitis), a metabolic liver disease, or livercancer.

Human uPA has two isoforms, both of which can be used in the diagnosticmethod of this invention. The GenBank Accession Numbers of isoform 1 isNP_(—)002649.1 (18 Oct. 2009) and isoform 2 is NP_(—)001138503.1 (18Oct. 2009). The GenBank Accession numbers of the other two novelmarkers, MMP9 and β-2MG, are NP_(—)004985.2 (22 Nov. 2009) andNP_(—)004039.1 (25 Oct. 2009), respectively.

To practice the method of this invention, a blood sample can be obtainedfrom a subject suspected of having liver fibrosis and the level of oneor more of the biomarkers mentioned above can be determined by aconventional method, e.g., ELISA and Westernblot. Data indicating thelevel(s) of the biomarker(s) is subjected to a suitable analysis (e.g.,discriminate function analysis, logistic regression analysis, ridgeregression analysis, or principal component analysis) to generate adisease score (e.g., represented by a numeric number) that characterizesthe blood profile of the biomarkers. When necessary, clinical factors(e.g., age and gender) can be taken into consideration. The diseasescore is then compared with a cutoff value that distinguishes presenceor absence of liver fibrosis or with a set of cutoff values thatdistinguish different fibrosis stages to assess whether the subject hasliver fibrosis and if so, in which disease stage. The cutoff values canbe determined by analyzing the blood profile of the same biomarkers viathe same analysis method in fibrosis-free subjects and indifferent-staged liver fibrosis patients. For example, it can be themiddle point between the disease score of fibrosis-free subjects andthat of fibrosis patients.

Described below is an exemplary procedure for determining theaforementioned cutoff values based on factors identified to beassociated with different staged fibrosis:

(1) assigning liver fibrosis patients to different groups according totheir disease conditions (e.g., fibrosis stages and risk factors);

(2) determining potential factors in the patients that possiblycorrelate with fibrosis stages;

(3) identifying those from the potential factors that differsignificantly among the different patient groups by univariate analysis;

(4) subjecting the identified factors to discriminant function analysis,logistic regression analysis, ridge regression analysis, or generalizedlinear model to assess the independent value of each factor in fibrosisdiagnosis;

(5) establishing a discriminant, ridge regression, or logisticregression model (e.g., a formula) to calculate a disease score based onthe identified factors (including fibrosis-associated biomarkers, aswell as clinical factors if applicable), and

(6) determining a cutoff value for each disease stage based on a diseasescore (e.g., mean value) representing each patient group, as well asother relevant factors, such as sensitivity, specificity, positivepredictive value (PPV) and negative predictive value (NPV).

The discriminant, ridge regression, or logistic regression modelestablished following the above procedure can be assessed for itsdiagnosis value by a receiver-operating characteristic (ROC) analysis tocreate a ROC curve. An optimal multivariable model provides a large Areaunder Curve (AUC) in the ROC analysis. See the models described inExamples 1-3 below.

Also within the scope of this invention is a kit used in theabove-described diagnostic method. This kit contains one or moreantibodies specific to fibrosis-associated biomarkers uPA, MMP9, β-2MG,and, optionally, others of interest (e.g., GOT, GPT, and AFP). In oneexample, the kit includes two different antibodies (i.e., a coatingantibody and a detecting antibody) that bind to the same biomarker.Typically, the detecting antibody is conjugated with a molecule whichemits a detectable signal either on its own or via binding to anotheragent. The term “antibody” used herein refers to a whole immunoglobulinor a fragment thereof, such as Fab or F(ab′)₂ that retainsantigen-binding activity. It can be naturally occurring or geneticallyengineered (e.g., single-chain antibody, chimeric antibody, or humanizedantibody).

The antibodies included in the kit of this invention can be obtainedfrom commercial vendors. Alternatively, they can be prepared byconventional methods. See, for example, Harlow and Lane, (1988)Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory, NewYork. To produce antibodies against a particular biomarker as listedabove, the marker, optionally coupled to a carrier protein (e.g., KLH),can be mixed with an adjuvant, and injected into a host animal.Antibodies produced in the animal can then be purified by affinitychromatography. Commonly employed host animals include rabbits, mice,guinea pigs, and rats. Various adjuvants that can be used to increasethe immunological response depend on the host species and includeFreund's adjuvant (complete and incomplete), mineral gels such asaluminum hydroxide, CpG, surface-active substances such as lysolecithin,pluronic polyols, polyanions, peptides, oil emulsions, keyhole limpethemocyanin, and dinitrophenol. Useful human adjuvants include BCG(bacille Calmette-Guerin) and Corynebacterium parvum. Polyclonalantibodies, i.e., heterogeneous populations of antibody molecules, arepresent in the sera of the immunized animal.

Monoclonal antibodies, i.e., homogeneous populations of antibodymolecules, can be prepared using standard hybridoma technology (see, forexample, Kohler et al. (1975) Nature 256, 495; Kohler et al. (1976) Eur.J. Immunol. 6, 511; Kohler et al. (1976) Eur J Immunol 6, 292; andHammerling et al. (1981) Monoclonal Antibodies and T Cell Hybridomas,Elsevier, N.Y.). In particular, monoclonal antibodies can be obtained byany technique that provides for the production of antibody molecules bycontinuous cell lines in culture such as described in Kohler et al.(1975) Nature 256, 495 and U.S. Pat. No. 4,376,110; the human B-cellhybridoma technique (Kosbor et al. (1983) Immunol Today 4, 72; Cole etal. (1983) Proc. Natl. Acad. Sci. USA 80, 2026, and the EBV-hybridomatechnique (Cole et al. (1983) Monoclonal Antibodies and Cancer Therapy,Alan R. Liss, Inc., pp. 77-96). Such antibodies can be of anyimmunoglobulin class including IgG, IgM, IgE, IgA, IgD, and any subclassthereof. The hybridoma producing the monoclonal antibodies of theinvention may be cultivated in vitro or in vivo. The ability to producehigh titers of monoclonal antibodies in vivo makes it a particularlyuseful method of production.

Moreover, antibody fragments can be generated by known techniques. Forexample, such fragments include, but are not limited to, F(ab′)₂fragments that can be produced by pepsin digestion of an antibodymolecule, and Fab fragments that can be generated by reducing thedisulfide bridges of F(ab′)₂ fragments.

Without further elaboration, it is believed that one skilled in the artcan, based on the above description, utilize the present invention toits fullest extent. The following specific embodiments are, therefore,to be construed as merely illustrative, and not limitative of theremainder of the disclosure in any way whatsoever. All publicationscited herein are incorporated by reference.

Example 1 Diagnosing Liver Fibrosis in HCV-Positive Patients Based onSerum Levels of uPA, MMP9, β-2MG, and Other Fibrosis-Related MarkersMaterials and Methods (i) Patients

140 HCV-positive patients (determined by a serology test via animmunoassay ELISA or RIA) and 93 healthy volunteers were participated inthis study. Both the patients and the healthy volunteers were randomlyassigned to a training set (n=148) and to a testing set (n=85). All ofthem were subjected to routine laboratory tests, including examinationof a liver panel (GOT/AST, GPT/ALT, total serum bilirubin, alkinephosphatase, and albumin), prothrombine time/international normalizedratio (INR), AFP, tests to exclude other causes of liver disease, liverultrasound, upper endoscopy, and modified Skinner survey (fordetermining alcohol consumption habit).

(ii) Biopsy Analysis

Liver biopsies were obtained from the patients and their histologicalfeatures were analyzed according to the METAVR scoring system. Briefly,these samples (more than 10 mm in length) were fixed, paraffin embedded,and stained with hematoxylin eosin safran and Masson's trichrome toidentifying lesions or picrosirius red for collagen detection. Thefibrosis stage (i.e., the amount of fibrosis) of each liver biopsy wasassessed according to the following criteria:

F0: no fibrosis (healthy),

F1: portal fibrosis without septa,

F2: few septa,

F3: numerous septa without cirrhosis, and

F4: cirrhosis.

The grade (i.e., the level of inflammation caused by HCV infection) ofeach biopsy was also determined following a conventional method.(iii) Serology Analysis

Ten millimeter of venous blood was drawn from each patient, collected ina tube without additives, and kept still at room temperature for 30minutes. The blood sample was then centrifuged at 4° C., 1600 g for 15minutes and the supernatant serum sample was collected.

The level of serum uPA was measured using the IMUBIND uPA ELISA kit(American Diagnostica inc), following the instructions provided by themanufacturer. Briefly, serum samples were diluted 1/20 in a samplediluent included in the kit. 100 μL of an uPA standard (provided in thekit) or a diluted sample were placed in a microtest plate pre-coatedwith a murine anti-uPA monoclonal antibody. The plate was sealed,incubated at 4° C. overnight, and then washed 4 times with a washbuffer. A biotinylated anti-uPA antibody was then added to the plate.After being incubated at room temperature for 1 hour, the plate waswashed and a streptavidin conjugated horse radish peroxidase (HRP) wasadded. One hour later, a solution containing a HRP substrate, i.e.,perborate/3′3,5,5,-tetramethylbenzidine (TMB), was added to the plate,which was kept at room temperature for 20 minutes to allow the enzymaticreaction take place. The reaction was stopped by addition of 50 μL 0.5NH₂SO₄ and the absorbance at 450 nm was measured using Spectramax M5(Molecular Devices). A standard calibration curve was constructed usinga four-parameter fit (SoftMax Pro software, Molecular Devices). Theserum uPA level was then determined based on the absorbance value versusthe standard curve. All measurements were performed in triplicateaccording to the manufacturer's instructions.

The serum level of MMP9 was determined by the Quantikine MMP9Immunoassay (R&D Systems, Minneapolis, Minn.). This assay is designed tomeasure the total amount of MMP-9, including both its 92 kDa precursorand 82 kDa mature forms. Briefly, a diluted serum sample (1:100) wasplaced in a microwell plate pre-coated with an anti-human MMP9 antibody.Two hours later, the plate was washed and a biotinylated anti-human MMP9antibody was added. The plate was included for one hour at roomtemperature and a streptavidin conjugated HRP was added followed byaddition of TMB. The enzymatic reaction was terminated by 1 mol/Lsulphuric acid and the absorbance at 450 nm-540 nm was measured usingmicroplate reader Spectramax M5 (Molecular Devices). The serum MMP9level was determined as described above based on the absorbance value.All measurements were performed in triplicate according to themanufacturer's instructions.

The serum β-2MG level was determined using a sandwich enzyme immunoassaykit (GenWay Biotech) as follows. 20 μL of a diluted serum sample (1:100)were placed in a microplate pre-coated with a mouse monoclonalanti-β-2MG antibody and mixed with 200 μL of a sample diluent. Themixture was incubated for 30 minutes at 37° C. The plate was washed 4times with distilled water and a HRP-conjugated sheep anti-β-2MGantibody was then added. After being incubated for 30 minutes at 37° C.,the plate was washed again followed by addition of TMB. 20 minuteslater, the enzymatic reaction was terminated by 1N HCl. The absorbanceat 450 nm was measured with Spectramax M5 (Molecular Devices) and theβ-2MG level was determined following the method described above. Allmeasurements were performed in triplicate according to themanufacturer's instructions.

The serum levels of other fibrosis-related markers, such as GOT, GPT, orAFP were determined by conventional methods.

(iv) Determining Disease Scores

The correlations between the expression level of uPA, MMP9, β-2MG, or acombination thereof and fibrosis stages were determined via discriminantanalysis, ridge regression analysis, or logistic regression analysis,taking into consideration clinical factors when applicable. Thediagnostic value for each of the three biomarkers or a combinationthereof was assessed based on sensitivity, specificity, positivepredictive values, and negative predictive values. The sensitivity,specificity, positive and negative predictive values were determined bya screen test, in which the points on the ROC curve that correspond todifferent cutoff values represent test positive. All statisticalanalysis was conducted with the R software.

Results (i) Patient Characteristics

The patient characteristics, including clinical factors obtained fromthe laboratory tests mentioned above and serum levels offibrosis-related biomarkers, were shown in Table 1 below:

TABLE 1 Patient Characteristics P-value Training set Testing set(univariate (n = 148) (n = 85) analysis) Age, mean (SD) 45.87 (14.53)50.26 (13.61) 0.02 Female, n (%)   84 (57%)   43 (51%) 0.44 FibrosisStage, n (%) No fibrosis (Healthy, F0)   63 (43%)   30 (35%) 0.34Fibrosis (F1 + F2 + F3)   55 (37%)   35 (41%) 0.64 Cirrhosis (F4)   30(20%)   20 (24%) 0.67 Serum Biochemical Makers, mean (SD) GOT/AST, IU/L45.28 (51.78) 57.63 (55.94) 0.11 GPT/ALT, IU/L 57.73 (81.9) 72.95(87.08) 0.19 T. Bilirubin, μmol/L 15.94 (20.55) 23.76 (19.46) 0.03Albumin, g/L  44.6 (5.6) 40.04 (6.2) 0.001 AFP, ng/ml  8.85 (17.09)19.96 (53.74) 0.11 Novel Serum Markers, mean (SD) uPA, ng/ml  0.82(0.59)  0.96 (0.78) 0.13 MMP9, μg/ml  0.22 (0.2)  0.22 (0.2) 0.88 β-2MG,μg/ml  2.17 (3.14)  1.82 (0.95) 0.20(ii) Association of Serum uPA, MMP9, or β-2MG and Other Clinical Factorswith Liver Fibrosis

As shown in Table 2 below, the serum level of uPA, MMP9, or β-2MGcorrelates with fibrosis presence/absence and severity in both trainingand testing patient sets.

TABLE 2 Association of Age, Gender, and Serum Biochemical Markers withLiver Fibrosis Training set (n = 148) Testing set (n = 85) P-valueP-value Healthy~F1 F2~F4 (univariate Healthy~F1 F2~F4 (univariate (n =73) (n = 75) analysis) (n = 40) (n = 45) analysis) Age, mean(SD) 37.59(11.31) 53.93 (12.7)  7.65E−14 44.1 (9.36) 55.73 (14.52) 3.03E−05Female, n (%) 46 (63%) 38 (51%) 0.18 17 (43%) 26 (58%) 0.23 GOT/AST,IU/L 21.37 (23.2) 72.98 (61.33) 1.677E−08 28.8 (22.4) 90.57 (64.35)3.03E−06 GPT/ALT, IU/L 25.96 (48.2) 89.94 (95.7) 1.701E−06 32.38 (44.2)109.84 (99.7) 1.07E−05 T. Bilirubin,, 11.18 (4.21) 21.54 (29.11) 0.115.18 (13.1) 26.12 (20.42) 0.09 μmol/L Albumin, g/L 46.87 (2.6) 36.18(5.73) 6.505E−07 45 (4.73) 38.55 (5.88) 0.020 AFP, ng/ml 2.98 (2.06)16.92 (24.1) 0.0001 3.84 (2.78) 40.86 (77.21) 0.019 Novel Serum Markers,mean(SD) uPA, ng/ml 0.48 (0.24) 1.14 (0.64) 3.988E−13 0.49 (0.21) 1.39(0.85) 8.25E−09 MMP9, μg/ml 0.3 (0.2) 0.14 (0.16) 2.653E−07 0.33 (0.24)0.11 (0.07) 9.77E−07 β-2MG, μg/ml 1.34 (0.51) 2.98 (4.25) 0.0013 1.22(0.38) 2.35 (0.99) 1.99E−09Low serum levels of uPA and β-2MG were observed in patients with no orlitter fibrosis while these levels elevated significantly in patientshaving mild or severe fibrosis. More specifically, in the training set,the mean serum levels of uPA in F0, F1, F2, F3, and F4 were found to be0.46 ng/ml, 0.61 ng/ml, 0.75 ng/ml, 0.86 ng/ml, and 1.66 ng/ml,respectively, and the mean serum levels of β-2MG in F0, F1, F2, F3, andF4 were found to be 1.26 μg/ml, 1.86 μg/ml, 2.22 μg/ml, 2.38 μg/ml, and4 μg/ml, respectively. On the other hand, healthy patients or patientswith litter fibrosis showed a significantly higher serum level of MMP9than patients with mild or severe fibrosis. The mean serum levels ofthis marker were found to be 0.33 μg/ml, 0.16 μg/ml, 0.19 μg/ml, 0.14μg/ml, and 0.1 μg/ml, respectively. Very similar data was obtained fromthe testing set. These results indicate that uPA, MMP9, and β-2MG,individually, are reliable markers for diagnosing liver fibrosis.(iii) Two-Marker Models for Diagnosing Liver Fibrosis

The results from this study indicate that the combined levels of any twoof uPA, MMP9, β-2MG, GOT, GPT, AFP and clinical factors can be used asreliable markers for diagnosing liver fibrosis. Shown below are twoexemplary two marker-models, i.e., uPA+MMP 9, and uPA+GPT, includingequations for calculating disease scores based on the combined levels ofeach two-marker pairs. These equations were established by discriminantfunction analysis, logistic regression analysis, or ridge regressionanalysis. Also shown below are tables (i.e., Tables 3-8) listing cutoffvalues, sensitivities, specificities, negative predictive values (NPV)and positive predictive values (PPV), and area under the ROC curve(AUROC) for these two-marker models.

uPA and MMP9 Discriminant Function Analysis:

Disease Score=1.4829×uPA (ng/ml)−3.2605×MMP9 (μg/ml)+5

TABLE 3 Cutoff Values Representing Different Fibrosis Stage in a uPA +MMP9 Discriminant Model Training set (n = 148) Testing set(n = 85)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 85(57%) 75 (51%) 52 (35%) 30 (20%) 55 (65%) 45 (53%) 36 (42%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 5.43105.6243 5.8955 6.0554 5.4310 5.6243 5.8955 6.0554 Sensitivity (%) 74 7271 80 78 80 75 85 Specificity (%) 90 90 90 90 97 95 90 80 NPV (%) 72 7685 95 71 81 83 95 PPV (%) 91 89 79 67 98 95 84 57 AUROC 0.9 0.89 0.890.93 0.98 0.96 0.93 0.91The coefficient value of uPA was 0.741 to 1.763, preferably 1.26 to1.705, and that of MMP9 was −7.553 to −2.839, preferably −3.75 to−2.771.

Logistic Regression Analysis:

Disease Score=exp (Logit_value)/(1+exp (Logit_value)), in whichLogit_value=−2.2416+3.2059×uPA (ng/ml)−5.6316×MMP9 (μg/ml)

TABLE 4 Cutoff Values Representing Different Fibrosis Stage in a uPA +MMP9 Logistic Regression Model Training set (n = 148) Testing set (n =85) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of85 (57%) 75 (51%) 52 (35%) 30 (20%) 55 (65%) 45 (53%) 36 (42%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 0.27040.3347 0.4548 0.5594 0.2704 0.3347 0.4548 0.5594 Sensitivity (%) 73 7273 80 78 80 75 85 Specificity (%) 90 90 90 90 97 95 90 80 NPV (%) 71 7686 95 71 81 83 95 PPV (%) 91 89 79 67 98 95 84 57 AUROC 0.9 0.89 0.890.93 0.97 0.96 0.94 0.91The coefficient of intercept was −3 to −1.48, preferably −2.578 to−1.905, the coefficient of uPA was 2.49 to 3.91, preferably 2.725 to3.687, and the coefficient of MMP9 was −8.01 to −3.24, preferably −6.477to −4.787.

Ridge Regression Analysis:

Disease Score=1.6641+1.7227×uPA (ng/ml)−1.9821×MMP9(μg/ml)

TABLE 5 Cutoff Values Representing Different Fibrosis Stage in a uPA +MMP9 Ridge Regression Model Training set (n = 148) Testing set (n = 85)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 85(57%) 75 (51%) 52 (35%) 30 (20%) 55 (65%) 45 (53%) 36 (42%) 20 (24%)patients Healthy, Healthy, Healthy, Healthy, Healthy, Healthy, HealthyF1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs. vs. vs.F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 2.5222 2.60292.9505 3.1213 2.5222 2.6029 2.9505 3.1213 Sensitivity (%) 72 75 67 80 7384 72 85 Specificity (%) 90 90 90 90 97 95 92 85 NPV (%) 70 78 83 95 6684 82 95 PPV (%) 91 89 78 67 98 95 87 63 AUROC 0.89 0.89 0.89 0.93 0.970.96 0.95 0.92The coefficient of intercept was 1.430 to 2.531, preferably 1.414 to1.914, that of uPA was 1.191 to 1.938, preferably 1.464 to 1.895, andthat of MMP9 was −4.428 to −1.501, preferably −2.279 to −1.685.

uPA and GPT Discriminant Function Analysis:

Disease Score=1.5351×uPA (ng/ml)+0.0083×GPT (IU/L)+5

TABLE 6 Cutoff Values Representing Different Fibrosis Stage in a uPA +GPT Discriminant Model Training set(n = 145) Testing set(n = 84)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 82(57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3, F4 F4 F1~F4 F2~F4 F3, F4 F4 Cutoff Value 6.30726.5753 7.1162 7.5766 6.3072 6.5753 7.1162 7.5766 Sensitivity (%) 79 7162 73 80 89 78 80 Specificity (%) 90 90 89 90 90 90 85 84 NPV (%) 77 7682 93 71 88 84 93 PPV (%) 92 88 76 65 93 91 80 62 AUROC 0.92 0.9 0.880.89 0.94 0.94 0.91 0.88The coefficient of uPA was 0.949 to 1.750, preferably 1.305 to 1.719,and that of GPT was 0.006 to 0.017, preferably 0.007 to 0.01.

Logistic Regression Analysis:

Disease Score=exp (Logit_value)/(1+exp (Logit_value)), in whichLogit_value=−3.7206+3.8376×uPA (ng/ml)+(−0.0001)×GPT (IU/L)

TABLE 7 Cutoff Values Representing Different Fibrosis Stage in a uPA +GPT Logistic Regression Model Training set(n = 145) Testing set(n = 84)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 82(57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3, F4 F4 F1~F4 F2~F4 F3, F4 F4 Cutoff Value 0.33330.4303 0.4316 0.5143 0.3333 0.4303 0.4316 0.5143 Sensitivity (%) 57 5062 80 67 59 69 85 Specificity (%) 90 90 89 90 93 95 94 88 Negativepredictive 62 65 82 94 61 68 80 95 value (%) Positive predictive 89 8476 67 94 93 89 68 value (%) Area under the 0.85 0.86 0.88 0.91 0.87 0.920.94 0.93 ROC curveThe coefficient of intercept was −4.30 to −3.14, preferably −4.279 to−3.274, that of uPA was 3.11 to 4.57, preferably 3.262 to 4.413, andthat of GPT was −0.01 to 0.002, preferably −0.00012 to −0.00008.

Ridge Regression Analysis:

Disease Score=0.9199+1.8321×uPA (ng/ml)+0.0034×GPT (IU/L)

TABLE 8 Cutoff Values Representing Different Fibrosis Stage in a uPA +GPT Ridge Regression Model Training set(n = 145) Testing set(n = 84)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 82(57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3, F4 F4 F1~F4 F2~F4 F3, F4 F4 Cutoff Value 2.37012.6192 2.9050 3.0855 2.3701 2.6192 2.9050 3.0855 Sensitivity (%) 72 6462 77 74 82 78 80 Specificity (%) 90 90 89 90 93 95 94 83 Negativepredictive 71 72 82 94 67 83 85 93 value (%) Positive predictive 91 8776 66 95 95 90 59 value (%) Area under the 0.89 0.89 0.88 0.9 0.93 0.940.94 0.92 ROC curveThe coefficient of intercept was 0.705 to 1.281, preferably 0.782 to1.03, that of uPA was 1.303 to 2.052, preferably 1.557 to 2.107, andthat of GPT was 0.002 to 0.009, preferably 0.0029 to 0.0039.

(iv) Three-Marker Models for Diagnosing Liver Fibrosis

Described below are exemplary three-marker models, based on the combinedserum levels of three markers selected from uPA, MMP9, β-2MG, GOT, GPT,and AFP for liver fibrosis diagnosis. When necessary, clinical factorswere also taken into consideration. These models were established bydiscriminate function analysis, logistic regression function analysis,and ridge regression function analysis. The disease scores calculatedfollowing these three-marker models were analyzed in view of fibrosisseverity. Linear correlations were found between METAVIR fibrosis stagesversus disease scores.

Four cutoff values indicating (i) any fibrosis (Healthy versus F1-F4);(ii) moderate fibrosis (Healthy˜F1 versus F2-F4); (iii) severe fibrosis(Healthy˜F2 versus F3-F4); and (iv) cirrhosis (Healthy˜F3 versus F4)were determined in the training set and validated in the testing set.

uPA, MMP9, and β-2MG

Discriminant Function Analysis:

Disease Score=1.4159×uPA (ng/ml)−3.0399×MMP9 (μg/ml)+0.0897×β-2MG(μg/ml)+5

TABLE 9 Cutoff Values Representing Different Fibrosis Stage in a uPA +MMP9 + β-2MG Discriminant Model Training set (n = 148) Testing set (n =85) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of85 (57%) 75 (51%) 52 (35%) 30 (20%) 55 (65%) 45 (53%) 36 (42%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 5.59185.7362 6.0726 6.2124 5.5918 5.7362 6.0726 6.2124 Sensitivity (%) 74 7371 83 78 82 75 85 Specificity (%) 90 90 90 90 97 95 92 85 NPV (%) 72 7785 95 71 83 83 95 PPV (%) 91 89 79 68 98 95 87 63 AUROC 0.91 0.9 0.90.94 0.98 0.96 0.94 0.92The coefficient of uPA was 0.389 to 1.604, preferably 1.204 to 1.586,that of MMP9 was −7.321 to −2.302, preferably −3.496 to −2.584, and thatof β-2MG was 0.048 to 1.114, preferably 0.076 to 0.103.

Logistic Regression Analysis:

Disease Score=exp (Logit_value)/(1+exp (Logit_value)), in which,Logit_value=−3.8614+2.8761×uPA (ng/ml)−4.0100×MMP9(μg/ml)+0.7853×β-2MG(μg/ml)

TABLE 10 Cutoff Values Representing Different Fibrosis Stage in a uPA +MMP9 + β-2MG Logistic Regression Model Training set (n = 148) Testingset (n = 85) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4Number of 85 (57%) 75 (51%) 52 (35%) 30 (20%) 55 (65%) 45 (53%) 36 (42%)20 (24%) patients (%) Healthy, Healthy, Healthy, Healthy, Healthy,Healthy, Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs.vs. vs. vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value0.1962 0.2806 0.4863 0.5961 0.1962 0.2806 0.4863 0.5961 Sensitivity (%)84 79 69 83 80 82 67 85 Specificity (%) 90 90 90 90 100 95 94 91 NPV (%)80 80 84 95 73 83 79 95 PPV (%) 92 89 78 68 100 95 89 74 AUROC 0.94 0.930.91 0.93 0.99 0.97 0.95 0.95The coefficient value of intercept was −4.9 to −2.28 and those of uPA,MMP9, and β-2MG were 2.15 to 3.6, −6.4 to −1.61, and 0.47 to 1.1,respectively. Preferably, the coefficient value of intercept, uPA, MMP9,and β-2MG were −4.441 to −3.282, 2.454 to 3.308, −4.611 to −3.409, and0.668 to 0.903, respectively.

Ridge Regression Analysis:

Disease Score=1.4645+1.6683×uPA (ng/ml)−1.7868×MMP9(μg/ml)+0.0926×β-2MG(μg/ml)

TABLE 11 Cutoff Values Representing Different Fibrosis Stage in a uPA +MMP9 + β-2MG Ridge Regression Model Training set (n = 148) Testing set(n = 85) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Numberof 85 (57%) 75 (51%) 52 (35%) 30 (20%) 55 (65%) 45 (53%) 36 (42%) 20(24%) patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 2.44832.5196 2.9199 3.0791 2.4483 2.5196 2.9199 3.0791 Sensitivity (%) 75 7769 83 75 87 72 85 Specificity (%) 90 90 90 90 97 95 92 86 NPV (%) 73 8084 95 67 86 82 95 PPV (%) 91 89 78 68 98 95 87 65 AUROC 0.91 0.9 0.90.93 0.97 0.96 0.95 0.92The coefficient of intercept was 0.558 to 2.418 (e.g., 1.245 to 1.684);those of uPA, MMP9, and β-2MG were 0.818 to 1.907 (e.g., 1.418 to1.835), −4.677 to −0.997 (e.g., −2.055 to −1.519), and 0.076 to 0.825(e.g., 0.079 to 0.106), respectively.

Table 12 below shows the results obtained from the testing set,following the three-marker models described above:

TABLE 12 Cutoff value Representing Different Fibrosis Stages DeterminedBased on Combined Serum Levels of uPA, MMP9, and β-2MG in Testing Set.Cut- Sensi- Speci- AUC Off tivity ficity PPV NPV Discriminant ModelHealthy vs. 0.98 5.21 0.98 0.83 0.92 0.96 F1~F4 5.25 0.96 0.93 0.96 0.93Healthy, 0.96 5.54 0.93 0.90 0.91 0.92 F1 vs. F2~F4 5.63 0.87 0.90 0.910.86 Healthy, 0.94 6.01 0.81 0.90 0.85 0.86 F1~F2 vs. F3~F4 6.10 0.750.92 0.87 0.83 Healthy, 0.92 6.62 0.85 0.91 0.74 0.95 F1~F3 vs. F4 6.780.70 0.92 0.74 0.91 Logistic Regression Model Healthy vs. 0.99 0.09 0.980.90 0.95 0.96 F1~F4 0.11 0.93 0.93 0.96 0.88 Healthy, 0.97 0.28 0.820.95 0.95 0.83 F1 vs. F2~F4 0.35 0.80 0.95 0.95 0.81 Healthy, 0.95 0.500.69 0.94 0.89 0.81 F1~F2 vs. F3~F4 0.54 0.67 0.94 0.89 0.79 Healthy,0.95 0.75 0.85 0.94 0.81 0.95 F1~F3 vs. F4 0.85 0.75 0.97 0.88 0.93Ridge Regression Model Healthy vs. 0.97 2.0 0.96 0.70 0.85 0.91 F1~F42.05 0.95 0.83 0.91 0.89 Healthy, 0.96 2.4 0.91 0.93 0.93 0.90 F1 vs.F2~F4 2.5 0.87 0.93 0.93 0.86 Healthy, 0.95 2.9 0.75 0.92 0.87 0.83F1~F2 vs. F3~F4 3.0 0.72 0.96 0.93 0.82 Healthy, 0.92 3.8 0.75 0.92 0.750.92 F1~F3 vs. F4 3.9 0.70 0.95 0.82 0.91

Based on the results shown above, suggested cutoff value ranges fordifferent disease stages were determined (see Table 13 below), takinginto consideration sensitivity, specificity, PPV, and NPV of thetraining set.

TABLE 13 Suggested Cutoff Values for Different Liver Fibrosis StageDisease Score Disease Score Disease Score Fibrosis (Discriminant(Logistic Regression (Ridge Regression stage Model) Model) Model)Healthy   0~5.21   0~0.09   0~2.00 Healthy~F1 5.21~5.26 0.09~0.112.00~2.05 F1 5.26~5.55 0.11~0.28 2.05~2.40 F1~F2 5.55~5.63 0.28~0.352.40~2.50 F2 5.63~6.01 0.35~0.50 2.50~2.90 F2~F3 6.01~6.10 0.50~0.542.90~3.00 F3 6.10~6.62 0.54~0.75 3.00~3.80 F3~F4 6.62~6.78 0.75~0.853.80~3.90 F4 6.78~ 0.85~1.00 3.90~uPA, MMP9, and GPT

Discriminant Function Analysis:

Disease Score=1.2295×uPA (ng/ml)+(−2.6571)×MMP9 (μg/ml)+0.0072×GPT(IU/L)+5

TABLE 14 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Discriminant Model Training set(n = 145) Testing set(n = 84)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 82(57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3, F4 F4 F1~F4 F2~F4 F3, F4 F4 Cutoff Value 5.47665.6061 6.5738 6.6547 5.4766 5.6061 6.5738 6.6547 Sensitivity (%) 85 8556 77 93 91 72 75 Specificity (%) 90 90 89 90 97 83 85 77 Negativepredictive 83 86 79 94 88 89 80 90 value (%) Positive predictive 92 9074 66 98 85 78 50 value (%) Area under the 0.95 0.92 0.89 0.91 0.99 0.960.91 0.86 ROC curveThe coefficient values of uPA, MMP9, and GPT were 0.539 to 1.456 (e.g.,1.045 to 1.414), −6.988 to −2.053 (e.g., −3.056 to −2.391), and 0.004 to0.014 (e.g., 0.006 to 0.008), respectively.

Logistic Regression Analysis:

Disease Score=exp (Logit-value)/(1+exp (Logit-value)), in whichLogit-value=−2.1715+3.3171×uPA(ng/ml)+(−6.2008)×MMP9(μg/ml)+(−0.0018)×GPT (IU/L)

TABLE 15 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Logistic Regression Model Training set(n = 145) Testing set(n =84) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of82 (57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~2 F1~3 Healthy F1 F1~2 F1~3 vs. vs. vs. vs. vs. vs. vs.vs. F1~F4 F2~F4 F3, F4 F4 F1~F4 F2~F4 F3, F4 F4 Cutoff Value 0.28030.3435 0.4456 0.5320 0.2803 0.3435 0.4456 0.5320 Sensitivity (%) 70 7176 80 70 75 75 85 Specificity (%) 90 90 89 90 96 95 90 81 Negativepredictive 70 76 88 94 64 78 83 95 value (%) Positive predictive 90 8879 67 97 94 84 58 value (%) Area under the 0.88 0.88 0.9 0.93 0.96 0.950.93 0.91 ROC curveThe coefficient value of intercept was −2.95 to −1.38 (e.g., −2.497 to−1.846) and those of uPA, MMP9, and GPT were 2.56 to 4.07 (e.g., 2.82 to3.649), −8.73 to −3.66 (e.g., −7.131 to −5.271), and −0.02 to 0.001(e.g., −0.0021 to −0.0015), respectively.

Ridge Regression Analysis:

Disease Score=1.5020+1.6479×uPA (ng/ml)−1.7885×MMP9(μg/ml)+0.0028×GPT(IU/L)

TABLE 16 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Ridge Regression Model Training set(n = 145) Testing set(n =84) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of82 (57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3, F4 F4 F1~F4 F2~F4 F3, F4 F4 Cutoff Value 2.38962.5208 2.9041 3.1454 2.3896 2.5208 2.9041 3.1454 Sensitivity (%) 77 7964 80 83 89 81 85 Specificity (%) 90 90 89 90 97 95 90 81 Negativepredictive 75 81 83 94 76 88 86 95 value (%) Positive predictive 91 8976 67 98 95 85 59 value (%) Area under the 0.92 0.9 0.89 0.92 0.97 0.960.94 0.92 ROC curveThe coefficient value of intercept was 1.154 to 2.300 (e.g., 1.277 to1.727) and those of uPA, MMP9, and GPT were 1.075 to 1.941 (e.g., 1.401to 1.895), −4.192 to −1.218 (e.g., −2.057 to −1.52), and 0.001 to 0.007(e.g., 0.0024 to 0.0032), respectively.

(v) Four-Marker Models for Diagnosing Liver Fibrosis

The results obtained from this study indicate that combinations of anyfour factors of uPA, MMP9, β-2MG, GOT, GPT, AFP, taking into accountclinical factors when applicable, are reliable markers for diagnosingliver fibrosis. Described below is an exemplary 4-marker model composedof uPA, MMP9, β-2MG, and GPT. The results are shown in Tables 17-19.

Discriminant Function Analysis:

Disease Score=1.1645×uPA (ng/ml)−2.4312×MMP9 (μg/ml)+0.0957×β-2MG(μg/ml)+0.0073×GPT (IU/L)+5

TABLE 17 Cutoff Values Representing Different Fibrosis Stage in a 4-Marker Discriminant Model Training set (n = 145) Testing set (n = 84)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 82(57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 5.62905.7334 6.8026 6.9384 5.6290 5.7334 6.8026 6.9384 Sensitivity (%) 87 8956 80 93 93 69 75 Specificity (%) 90 90 89 90 97 85 90 80 NPV (%) 84 8979 94 88 92 80 91 PPV (%) 92 90 74 67 98 87 83 54 AUROC 0.96 0.93 0.90.91 0.99 0.96 0.91 0.87The coefficient vales of uPA, MMP9, β-2MG, and GPT were 0.196 to 1.376(e.g., 0.99 to 1.339), −6.684 to −1.623 (e.g., −2.796 to −2.067), 0.055to 0.974 (e.g., 0.081 to 0.11), and 0.004 to 0.012 (e.g., 0.0062 to0.0084), respectively.

Logistic Regression Analysis:

Disease Score=exp (Logit_value)/(1+exp (Logit_value)), in whichLogit_value=−3.6742+3.0107×uPA (ng/ml)−4.4549×MMP9 (μg/ml)+0.7074×β-2MG(μg/ml)+−0.0017×GPT (IU/L)

TABLE 18 Cutoff Values Representing Different Fibrosis Stage in a4-Marker Logistic Regression Model Training set (n = 145) Testing set (n= 84) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of82 (57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 0.19050.2895 0.4522 0.5775 0.1905 0.2895 0.4522 0.5775 Sensitivity (%) 78 7674 83 74 82 67 85 Specificity (%) 90 90 89 90 100 95 94 89 NPV (%) 76 8087 95 68 83 79 95 PPV (%) 91 89 79 68 100 95 89 71 AUROC 0.92 0.92 0.920.94 0.98 0.96 0.95 0.95The coefficient value of intercept was −4.74 to −2.61 (e.g., −4.225 to−3.123) and those of uPA, MMP9, β-2MG, and GPT were 2.24 to 3.77 (e.g.,2.559 to 3.462), −6.99 to −1.92 (e.g., −5.123 to −3.787), 0.39 to 1.02(e.g., 0.6013 to 0.8135), and −0.004 to 0.001 (e.g., −0.002 to −0.001).

Ridge Regression Analysis:

Disease Score=1.2866+1.5874×uPA (ng/ml)−1.5725×MMP9(μg/ml)+0.0955×β-2MG(μg/ml)+0.0029×GPT (IU/L)

TABLE 19 Cutoff Values Representing Different Fibrosis Stage in a4-Marker Ridge Regression Model Training set (n = 145) Testing set (n =84) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of82 (57%) 72 (50%) 50 (34%) 30 (21%) 54 (64%) 44 (52%) 36 (43%) 20 (24%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 2.34992.5306 2.9141 3.0809 2.3499 2.5306 2.9141 3.0809 Sensitivity (%) 78 7868 83 83 86 78 85 Specificity (%) 90 90 89 90 97 95 90 81 NPV (%) 76 8084 95 76 86 84 95 PPV (%) 91 89 77 68 98 95 85 59 AUROC 0.93 0.91 0.90.93 0.98 0.96 0.95 0.92The coefficient value of intercept was 0.297 to 2.109 (e.g., 1.094 to1.48), and those of uPA, MMP9, β-2MG, and GPT were 0.748 to 1.800 (e.g.,1.349 to 1.778), −3.919 to −0.776 (e.g., −1.808 to −1.337), 0.077 to0.830 (e.g., 0.0812 to 0.1098), and 0.001 to 0.007 (e.g., 0.0025 to0.0033).

(vi) Five-Marker Models for Diagnosing Liver Fibrosis

The results obtained from this study indicate that combinations of anyfive factors of uPA, MMP9, β-2MG, GOT, GPT, AFP, taking intoconsideration clinical factors where applicable, are reliable markersfor diagnosing liver fibrosis. Described below is an exemplary 5-markermodel composed of uPA, MMP9, β-2MG, GPT, GOT. The results are shown inTables 20-22.

Discriminant Function Analysis:

Disease  Score = 1.1009 × uPA  (ng/ml) − 2.2941 × MMP 9  (μg/ml) + 0.0974 × β − 2MG  (μg/ml) + 0.0065 × GPT  (IU/L) + 0.0024 × GOT(IU/L) + 5

TABLE 20 Cutoff Values Representing Different Fibrosis Stage in a5-Marker Discriminant Model Training set (n = 133) Testing set (n = 75)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 70(53%) 60 (45%) 39 (29%) 19 (14%) 45 (60%) 35 (47%) 27 (36%) 11 (15%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 5.64105.7987 6.9746 7.0015 5.6410 5.7987 6.9746 7.0015 Sensitivity (%) 86 8746 74 93 91 63 73 Specificity (%) 90 90 90 90 97 85 92 81 NPV (%) 85 8980 95 91 92 81 95 PPV (%) 91 88 67 56 98 84 81 40 AUROC 0.95 0.92 0.890.9 0.99 0.95 0.91 0.88The coefficient values of uPA, MMP9, β-2MG, GPT, GOT were −0.070 to1.512 (e.g., 0.936 to 1.266), −6.453 to −1.468 (e.g., −2.638 to −1.95),0.058 to 1.209 (e.g., 0.083 to 0.112), −0.002 to 0.019 (e.g., 0.0055 to0.0075), and −0.011 to 0.025 (e.g., 0.002 to 0.0028), respectively.

Logistic Regression Analysis:

Disease Score=exp (Logit_value)/(1+exp (Logit_value)), in whichLogit_value=−3.4751+2.7416×uPA (ng/ml)−4.5237×MMP9 (μg/ml)+0.6952×β-2MG(μg/ml)−0.0021×GPT (IU/L)+0.0007×GOT (IU/L)

TABLE 21 Cutoff Values Representing Different Fibrosis Stage in a5-Marker Logistic Regression Model Training set (n = 133) Testing set (n= 75) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of70 (53%) 60 (45%) 39 (29%) 19 (14%) 45 (60%) 35 (47%) 27 (36%) 11 (15%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 0.19480.2854 0.4667 0.5551 0.1948 0.2854 0.4667 0.5551 Sensitivity (%) 74 7264 79 69 77 59 82 Specificity (%) 90 90 90 90 100 95 94 89 NPV (%) 76 8086 96 68 83 80 97 PPV (%) 90 86 74 58 100 93 84 56 AUROC 0.91 0.91 0.90.91 0.98 0.95 0.94 0.93The coefficient value of intercept was −4.54 to −2.4 (e.g., −3.996 to−2.954) and those of uPA, MMP9, β-2MG, GPT, and GOT were 1.87 to 3.61(e.g., 2.33 to 3.153), −7.13 to −1.9 (e.g., −5.202 to −3.845), 0.38 to1.01 (e.g., 0.5909 to 0.7995), −0.01 to 0.01 (e.g., −0.0024 to −0.0018),and −0.01 to 0.01 (e.g., 0.0006 to 0.0008), respectively.

Ridge Regression Analysis:

Disease Score=1.2750+1.3505×uPA (ng/ml)−1.4346×MMP9(μg/ml)+0.0978×β-2MG(μg/ml)+0.0004×GPT (IU/L)+0.0056×GOT (IU/L)

TABLE 22 Cutoff Values Representing Different Fibrosis Stage in a5-Marker Ridge Regression Model Training set (n = 133) Testing set (n =75) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of70 (53%) 60 (45%) 39 (29%) 19 (14%) 45 (60%) 35 (47%) 27 (36%) 11 (15%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 2.20062.3922 2.9473 3.1712 2.2006 2.3922 2.9473 3.1712 Sensitivity (%) 79 7754 79 87 91 70 82 Specificity (%) 90 90 90 90 97 93 94 81 NPV (%) 79 8283 96 83 93 85 96 PPV (%) 90 87 70 58 98 91 86 43 AUROC 0.93 0.91 0.890.91 0.98 0.96 0.94 0.91The coefficient value of intercept comprised was 0.145 to 1.909 (e.g.,1.084 to 1.466) and those of uPA, MMP9, β-2MG, GPT, and GOT were 0.576to 1.826 (e.g., 1.148 to 1.553), −3.676 to −0.603 (e.g., −1.65 to−1.219), 0.077 to 0.862 (e.g., 0.0831 to 0.1125), −0.005 to 0.009 (e.g.,0.0003 to 0.0004), and −0.008 to 0.021 (e.g., 0.0047 to 0.0065),respectively.(vii) Six-Marker Models for Diagnosing Liver Fibrosis

The results obtained from this study indicate that a combination of uPA,MMP9, β-2MG, GOT, GPT, and AFP, taking into account clinical factorswhen necessary, is a reliable marker for diagnosing liver fibrosis.Shown below are equations for calculating disease scores (established bydiscriminant analysis, logistic regression analysis, and ridgeregression analysis) based on this 6-marker combinations and cutoffvalues for different fibrosis stages (see Tables 23-25 below).

Discriminant Function Analysis:

Disease  Score = 1.4401 × uPA  (ng/ml) − 1.2831 × MMP 9  (μg/ml) + 0.0921 × β − 2MG  (μg/ml) − 0.0099 × AFP  (ng/ml) + 0.0129 × GPT  (IU/L) − 0.0004 × GOT(IU/L) + 5

TABLE 23 Cutoff Values Representing Different Fibrosis Stage in a6-Marker Discriminant Model Training set (n = 109) Testing set (n = 53)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of 4646(%) 39 (36%) 27 (25%) 16 (15%) 24 (45%) 18 (34%) 14 (26%) 11 (21%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 6.17276.3100 6.6171 7.7270 6.1727 6.3100 6.6171 7.7270 Sensitivity (%) 89 8281 75 88 94 86 73 Specificity (%) 90 90 90 90 93 91 87 86 NPV (%) 92 9094 95 90 97 94 92 PPV (%) 87 82 73 57 91 85 71 57 AUROC 0.94 0.9 0.910.9 0.98 0.96 0.91 0.89The coefficient values of uPA, MMP9, β-2MG, AFP, GPT, and GOT were 0.141to 1.923 (e.g., 1.224 to 1.656), −5.052 to −0.393 (e.g., −1.476 to−1.091), 0.069 to 1.303 (e.g., 0.078 to 0.106), −0.032 to 0.054 (e.g.,−0.0114 to −0.0084), 0.002 to 0.032 (e.g., 0.011 to 0.0148), and −0.023to 0.021 (e.g., −0.00046 to −0.00034), respectively.

Logistic Regression Analysis:

Disease Score=exp (Logit_value)/(1+exp (Logit_value)), in whichLogit_value=−4.1023+2.4436×uPA (ng/ml)−6.8921×MMP9 (μg/ml)+1.2869×β-2MG(μg/ml)−0.0112×AFP (ng/ml)−0.0015×GPT (IU/L)+0.0018×GOT (IU/L)

TABLE 24 Cutoff Values Representing Different Fibrosis Stage in a6-Marker Logistic Regression Model Training set (n = 109) Testing set (n= 53) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of46 46(%) 39 (36%) 27 (25%) 16 (15%) 24 (45%) 18 (34%) 14 (26%) 11 (21%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 0.15690.1617 0.3392 0.5628 0.1569 0.1617 0.3392 0.5628 Sensitivity (%) 74 8281 75 67 89 86 91 Specificity (%) 90 90 90 90 100 100 95 98 NPV (%) 8390 94 95 78 95 95 91 PPV (%) 85 82 73 57 100 100 86 98 AUROC 0.91 0.930.94 0.93 0.99 1 0.99 0.98The coefficient value of intercept was −5.6 to −2.61 (e.g., −4.718 to−3.487) and those of uPA, MMP9, β-2MG, GPT, GOT, and AFP were 1.38 to3.5 (e.g., 2.077 to 2.81), −10.86 to −2.92 (e.g., −7.926 to −5.858),0.82 to 1.75 (e.g., 1.0939 to 1.4799), −0.01 to 0.01 (e.g., −0.0017 to−0.0012), −0.01 to 0.01 (e.g., 0.0015 to 0.002), and −0.01 to 0.02(e.g., −0.01 to −0.0095), respectively.

Ridge Regression Analysis:

Disease Score=0.9632+1.4215×uPA (ng/ml)−1.0722×MMP9(μg/ml)+0.0986×β-2MG(μg/ml)−0.0053×AFP (ng/ml)+0.0019×GPT (IU/L)+0.0058×GOT (IU/L)

TABLE 25 Cutoff Values Representing Different Fibrosis Stage in a6-Marker Ridge Regression Model Training set(n = 109) Testing set(n =53) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Number of46 46(%) 39 (36%) 27 (25%) 16 (15%) 24 (45%) 18 (34%) 14 (26%) 11 (21%)patients (%) Healthy, Healthy, Healthy, Healthy, Healthy, Healthy,Healthy F1 F1~F2 F1~F3 Healthy F1 F1~F2 F1~F3 vs. vs. vs. vs. vs. vs.vs. vs. F1~F4 F2~F4 F3~F4 F4 F1~F4 F2~F4 F3~F4 F4 Cutoff Value 2.04242.1525 2.5894 2.9761 2.0424 2.1525 2.5894 2.9761 Sensitivity (%) 76 8270 81 83 100 93 73 Specificity (%) 90 90 90 90 93 94 87 93 NPV (%) 84 9090 97 87 100 97 93 PPV (%) 85 82 70 59 91 90 72 73 AUROC 0.92 0.91 0.920.91 0.99 0.99 0.96 0.95The coefficient value of intercept was −0.336 to 1.587 (e.g., 0.819 to1.108) and those of uPA, MMP9, β-2MG, AFP, GPT, and GOT were 0.396 to2.024 (e.g., 1.208 to 1.635), −2.763 to −0.256 (e.g., −1.239 to −0.916),0.087 to 1.034 (e.g., 0.088 to 0.113), −0.021 to 0.037 (e.g., −0.0061 to−0.0045), −0.006 to 0.015 (e.g., 0.0016 to 0.0021), and −0.014 to 0.024(e.g., 0.0049 to 0.0066), respectively.

All of the above mentioned models were validated in the test set andsimilar results, including cutoff values, sensitivity, specificity, NPV,PPV, and AUROC, were observed.

Example 2 Diagnosing Liver Fibrosis in HBV-Positive Patients Based onSerum Levels of uPA, MMP9, and β-2MG

The single- and 3-marker models were also validated in 30 patientscarrying HBV and 30 healthy subjects in this study. Table 26 below listscharacteristics of this data set.

TABLE 26 Patient Characteristics Data set (n = 60) Age, mean (SD) 44.23(9.27) Female, n (%)   17 (28%) Serum Biochemical Makers, mean (SD)GOT/AST, IU/L 54.53 (59.56) GPT/ALT, IU/L 87.02 (129.47) T. Bilirubin,μmol/L 21.52 (21.78 Albumin, g/L  42.3 (5.88) AFP, ng/ml  8.73 (26.62)Novel Serum Markers, mean (SD) uPA, ng/ml  0.73 (0.6) MMP9, μg/ml  0.27(0.22) β-2MG, μg/ml  1.44 (1.16)As shown in Table 27 below, the serum level of each of uPA, MMP9, andβ-2MG correlates with fibrosis severity:

TABLE 27 Serum Levels of uPA, MMP9, and β-2MG in HBV-Positive PatientsuPA (ng/ml) MMP9 (μg/ml) β-2MG (μg/ml) Healthy (n = 30) 0.46 (0.18)  0.4(0.23)  1.1 (0.18) F1 (n = 9) 0.87 (0.53) 0.18 (0.04) 1.17 (0.4) F2 (n =3) 0.42 (0.1) 0.08 (0.01) 1.13 (0.26) F3 (n = 7) 0.77 (0.35) 0.13 (0.06)1.42 (0.42) F4 (n = 11) 1.45 (0.93) 0.11 (0.05) 2.68 (2.34) *Number ofhealthy: 30 subjects (testing set);

The cutoff values representing different fibrosis stages based ondisease scores calculated following the equations described in the3-Marker model in Example 1 above were shown in Tables 28-30 below (thehealthy test set mentioned above was subjected to this study):

TABLE 28 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Discriminant Model for HBV-Positive Patients Data set (n = 60)Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 Number of patients 30 21 18 11 (%)(50%) (35%) (30%) (18%) Healthy, Healthy, Healthy, Healthy F1 F1~F2F1~F3 vs. vs. vs. vs. F1~F4 F2~F4 F3, F4 F4 Cutoff Value 5.5918 5.73626.0726 6.2124 Sensitivity (%) 53 57 56 64 Specificity (%) 97 92 95 92NPV (%) 67 80 83 92 PPV (%) 94 80 83 64 AUROC 0.96 0.89 0.89 0.88

TABLE 29 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Logistic Regression Model for HBV-Positive Patients Data set (n= 60) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 Number of patients 30 21 18 11(%) (50%) (35%) (30%) (18%) Healthy, Healthy, Healthy, Healthy F1 F1~F2F1~F3 vs. vs. vs. vs. F1~F4 F2~F4 F3, F4 F4 Cutoff Value 0.1962 0.28060.4863 0.5961 Sensitivity (%) 57 52 50 64 Specificity (%) 100 95 95 96NPV (%) 70 79 82 92 PPV (%) 100 85 82 78 AUROC 0.95 0.88 0.89 0.88

TABLE 30 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Ridge Regression Model for HBV-Positive Patients Data set (n =60) Fibrosis Stage F1~F4 F2~F4 F3~F4 F4 Number of patients (%) 30 21 1811 (50%) (35%) (30%) (18%) Healthy, Healthy, Healthy, Healthy F1 F1~F2F1~F3 vs. vs. vs. vs. F1~F4 F2~F4 F3, F4 F4 Cutoff Value 2.0620 2.45712.4571 2.7726 Sensitivity (%) 90 57 67 73 Specificity (%) 90 90 90 90NPV (%) 90 80 86 94 PPV (%) 90 75 75 62 AUROC 0.94 0.85 0.88 0.86

Example 3 Diagnosing Liver Fibrosis in Patients Having Alcohol-RelatedLiver Disease Based on Serum Levels of uPA, MMP9, and β-2MG

53 patients having alcohol-related liver diseases were participated and30 healthy subjects in this study. The patient characteristics arelisted in Table 31 below. These patients were subjected to routinelaboratory tests as described in Example 1 above and also modifiedSkinner survey to determine their alcohol consumption habit.

TABLE 31 Patient Characteristics Data set (n = 83) Age, mean (SD) 43.83(9.23) Female, n (%) 15 (18%) Serum Biochemical Makers, mean (SD)GOT/AST, IU/L 67.88 (71.87) GPT/ALT, IU/L 46.28 (57.22) T. Bilirubin,μmol/L 53.31 (50.8) Albumin, g/L 35.02 (7.15) AFP, ng/ml 103.81 (892.5)Novel Serum Markers, mean (SD) uPA, ng/ml 1.11 (1.07) MMP9, μg/ml 0.3(0.24) β-2MG, μg/ml 1.77 (1.11)

Serum levels of uPA, MMP9, and β-2MG in these patients were examinedfollowing the ELISA assays described in Example 1. The results thusobtained are shown below Table 32.

TABLE 32 Serum Levels of uPA, MMP9, and β-2MG in Patients Suffering fromAlcohol-Related Disease uPA (ng/ml) MMP9 (μg/ml) β-2MG (μg/ml) Healthy(n = 30) 0.46 (0.18)  0.4 (0.23)  1.1 (0.18) Fatty Liver (n = 15) 0.57(0.27) 0.37 (0.28) 1.84 (0.84) Hepatitis (n = 7) 1.12 (0.93) 0.27 (0.19)1.91 (0.66) F4 (n = 31)   2 (1.23) 0.17 (0.17) 2.36 (1.45)

Disease scores of these patients were calculated following the 3-markerequation described in Example 1 above and cutoff values for differentfibrosis stages are shown in Table 33 below:

TABLE 33 Cutoff Values Representing Different Fibrosis Stage in a3-Marker Model for Parients Having Alcohol-Related Disease Data set (n =83) Discriminant Logistic Ridge regression model regression model modelFibrosis Stage F4 Number of patients 31 (37%) 31 (37%) 31 (37%) (%)Healthy, Fatty_liver, Alcoholic_Hepatitis vs. F4 Cutoff Value 5.64300.2606 2.5453 Sensitivity (%) 90 90 94 Specificity (%) 90 90 90 NPV (%)94 94 96 PPV (%) 85 85 85 AUROC 0.93 0.94 0.95 *Number of healthy: 30subjects (testing set); Number of Fatty_liver and Alcoholic_Hepatitis:22 subjects

Other Embodiments

All of the features disclosed in this specification may be combined inany combination. Each feature disclosed in this specification may bereplaced by an alternative feature serving the same, equivalent, orsimilar purpose. Thus, unless expressly stated otherwise, each featuredisclosed is only an example of a generic series of equivalent orsimilar features.

From the above description, one skilled in the art can easily ascertainthe essential characteristics of the present invention, and withoutdeparting from the spirit and scope thereof, can make various changesand modifications of the invention to adapt it to various usages andconditions. Thus, other embodiments are also within the claims.

What is claimed is:
 1. A method of diagnosing liver fibrosis in a humansubject, comprising: obtaining a blood sample from a human subjectsuspected of having liver fibrosis, detecting an expression level(s) ofone or more of urokinase-type plasminogen activator (uPA), matrixmetalloproteinase 9 (MMP9), and β-2-microglobulin (β-2MG) in the bloodsample, calculating a disease score based on the expression levels; anddetermining whether the subject has fibrosis based on the disease score.2. The method of claim 1, wherein the blood sample is a serum sample. 3.The method of claim 1, wherein the human subject is selected from thegroup consisting of a hepatitis C virus carrier, a hepatitis B viruscarrier, a patient suffering from an alcohol-related liver disease, anda patient suffering from a metabolic liver disease.
 4. The method ofclaim 1, wherein the detecting step is performed by examining theexpression levels of two of uPA, MMP9, and β-2MG in the blood sample. 5.The method of claim 1, wherein the detecting step is performed byexamining the expression levels of uPA, MMP9, and β-2MG in the bloodsample.
 6. The method of claim 5, wherein the blood sample is a serumsample.
 7. The method of claim 5, wherein the calculating step isperformed by subjecting the expression levels of the uPA, MMP9, andβ-2MG to discriminant function analysis, logistic regression analysis,or ridge regression analysis.
 8. The method of claim 5, furthercomprising, after the subject being determined to have liver fibrosis,assessing the subject's disease stage based on the disease score ascompared to pre-determined cutoff values indicating different fibrosisstages.
 9. The method of claim 8, wherein the disease score iscalculated by discriminant function analysis, logistic regressionanalysis, or ridge regression analysis.
 10. The method of claim 1,further comprising detecting in the blood sample expression levels of(a) one or more markers selected from the group consisting of glutamicoxaloacetic transaminase (GOT), glutamic pyruvic transaminase (GPT), andalpha-fetoprotein (AFP).
 11. The method of claim 10, wherein the bloodsample is a serum sample.
 12. The method of claim 10, wherein thecalculating step is performed by subjecting the expression levels of todiscriminant function analysis, logistic regression analysis, or ridgeregression analysis.
 13. A kit for diagnosing liver fibrosis, comprisinga first antibody specifically binding to urokinase-type plasminogenactivator (uPA), a second antibody specifically binding to matrixmetalloproteinase 9 (MMP9), and a third antibody specifically binding toβ-2-microglobulin (β-2MG).
 14. The kit of claim 13, further comprisingan antibody specifically binding to glutamic oxaloacetic transaminase(GOT), an antibody specifically binding to glutamic pyruvic transaminase(GPT), an antibody specifically binding to alpha-fetoprotein (AFP), or acombination thereof.
 15. The kit of claim 13, wherein the antibodies arewhole immunoglobulin molecules.
 16. The kit of claim 13, consistingessentially of the first antibody, the second antibody, and the thirdantibody.
 17. The kit of claim 14, consisting essentially of the firstantibody, the second antibody, the third antibody, the antibodyspecifically binding to GOT, the antibody specifically binding to GPT,and the antibody specifically binding to AFP).
 18. The kit of claim 17,wherein the antibodies are whole immunoglobulin molecules.